Selecting time-series data for information technology (it) operations analytics anomaly detection

ABSTRACT

A computer program product for selecting time-series data is provided. The computer program product includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and executable by a processing circuit to cause the processing circuit to assemble, by the processing circuit, a set of analytic assessment tools for time-series data, engage, by the processing circuit, the analytic assessment tools to measure characteristics-of-importance in a relevant analytic domain for sets of the time-series data, generate, by the processing circuit, as a measurement result, a score for each set of the time-series data based on the associated characteristics-of-importance and rank, by the processing circuit, the sets of the time-series data in accordance with the score for each set of the time-series data for subsequent time-series data selection.

BACKGROUND

The present invention relates to time-series data selection and, morespecifically, to time-series data selection for use in detectinganomalies in information technology (IT) operations analytics.

When analytics systems focusing on analyses of time-series data aredeployed in an IT environment, an early step in the deployment andconfiguration of such systems is the selection of a subset of data thatwill work well or be suitable for a given type of analysis from theavailable data. For example, when an analytic tool to retrieve andanalyze data from a IT performance management system is deployed, theadministrator or user responsible for the deployment may be required toexplicitly select which tables of data to export (if they are containedwithin a database) or to specify which data to export.

In general though, what works well and provides the best possibleresults in one situation versus another situation is highly dependent onthe actual analytic algorithms that are deployed but the administratorsor users that are charged with the deployment and the configurations mayhave no real sense of what data should be selected. Thus a commonapproach to system deployment and configuration is based simply upon thenotion of “best practices” in which previous experience and heuristicsare codified in documentation which provide recommendations on whichmetrics to process. A variation on this theme is one where code andconfigurations to extract data are organized in deployablepackages/packs that can be deployed together.

SUMMARY

According to an embodiment of the present invention, a computer programproduct for selecting time-series data is provided. The computer programproduct includes a computer readable storage medium having programinstructions embodied therewith. The program instructions are readableand executable by a processing circuit to cause the processing circuitto assemble, by the processing circuit, a set of analytic assessmenttools for time-series data, engage, by the processing circuit, theanalytic assessment tools to measure characteristics-of-importance in arelevant analytic domain for sets of the time-series data, generate, bythe processing circuit, as a measurement result, a score for each set ofthe time-series data based on the associatedcharacteristics-of-importance and rank, by the processing circuit, thesets of the time-series data in accordance with the score for each setof the time-series data for subsequent time-series data selection.

According to another embodiment of the present invention, a computerprogram product for selecting time-series data is provided and includesa computer readable storage medium having stored thereon first programinstructions executable by a processing circuit to cause the processingcircuit to assemble a set of analytic assessment tools for time-seriesdata, second program instructions executable by the processing circuitto cause the processing circuit to engage the analytic assessment toolsto measure characteristics-of-importance in a relevant analytic domainfor sets of the time-series data, third program instructions executableby the processing circuit to cause the processing circuit to generate,as a measurement result, a score for each set of the time-series databased on the associated characteristics-of-importance and fourth programinstructions executable by the processing circuit to cause theprocessing circuit to rank the sets of the time-series data inaccordance with the score for each set of the time-series data forsubsequent time-series data selection.

According to yet another embodiment of the present invention, acomputer-implemented method for selecting time-series data is providedand includes assembling a set of analytic assessment tools fortime-series data, engaging the analytic assessment tools to measurecharacteristics-of-importance in a relevant analytic domain for sets ofthe time-series data, generating, as a measurement result, a score foreach set of the time-series data based on the associatedcharacteristics-of-importance and ranking the sets of the time-seriesdata in accordance with the score for each set of the time-series datafor subsequent time-series data selection.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 is a schematic diagram of a computing system in accordance withembodiments;

FIG. 2 is a schematic diagram of a computer program product of thecomputing system of FIG. 1 in accordance with embodiments;

FIG. 3 is a flow diagram illustrating a deployment process for thecomputer program product of FIG. 2 in accordance with embodiments; and

FIG. 4 is a flow diagram illustrating a computer-implemented method ofselecting time-series data in accordance with embodiments.

DETAILED DESCRIPTION

Deploying and configuring analytics systems can be problematic due tothe fact that best practices for the same are limited to being attunedto conditions that have already been seen before multiple times and thefact that this limitation may be most acute at the early stages of theanalytic product lifecycle. Other problems include the fact thatapplicability of best practices depends on the dynamics of a givenenvironment since what worked well in one environment sometimes will notwork well in another environment and the fact that the applicability ofbest practices changes with algorithm changes and, depending on thedynamics of the environment, they may not apply or become tedious toupdate in any effect.

Therefore, the description provided below relates to an approach toquickly analyze actual data in source systems and to determine a set ofacceptable metrics or metric types based upon what algorithms have beendeployed. This set of acceptable metrics should then be presented to anadministrator or user so that the administrator or user is given anopportunity to select which ones of the set of acceptable metrics shouldbe processed and which ones should be ignored. The motivation for theuser selection would be that there are additional selection criteriabeyond the notion of “what works well with the algorithm,” which shouldbe or must be considered. For example, the administrator or the user maywant to give particular consideration towards concerns of computingresources, scalability and customer interest.

The approach effectively combines a method for assessing/scoringtime-series against a variety of criteria (e.g. data completeness,presence of particular frequency components, etc.), computing a weightedscore for each time-series in the data source and then presenting to theadministrator or user the time-series ordered by this ranking forselection. The assessment/scoring schemes themselves may beindependently derivable or otherwise produced by algorithm developers.In any case, through the application of the appropriate time-seriesassessment/scoring schemes, individual time-series/metric types can beclassified as “acceptable” or “unacceptable.” This would be a two stageprocess that would first do a cursory search on a small time window todetermine if the data should be considered for analytics, with thesecond stage expanding the time window and focusing on the data that wasdeemed valuable.

With reference to FIG. 1, a computing system 10 is provided and may beconfigured for example as an enterprise computing system or as apersonal computing system. In either case, the computing system 10includes multiple computing devices 11, 12, 13, etc., which areconfigured to be networked together for communication purposes. Each ofthe multiple computing devices 11, 12, 13, etc., includes among otherfeatures a processing circuit 20, a display 30, user input devices 40and a networking unit 50 as well as a computer program product 100 forselecting time-series data. The processing circuit 20 may be provided asa micro-processor, a central processing unit (CPU) or any other suitableprocessing device. The display 30 may be provided as a monitor and isconfigured to display data and information as well as a graphical userinterface to an administrator or user. The user input devices 40 may beprovided as a mouse and a keyboard combination and are configured toallow the administrator or user to input commands to the processingcircuit 20. The networking unit 50 may be provided as an Ethernet orother suitable networking device by which the multiple computing devices11, 12, 13, etc. are communicative.

With reference to FIG. 2, the computer program product 100 includes acomputer readable storage medium 110 having first, second, third andfourth program instructions 111, 112, 113 and 114 stored thereon. Thefirst program instructions 111 are executable by the processing circuit20 of each of the multiple computing devices 11, 12, 13, etc., to causethe processing circuit 20 to assemble a set of analytic assessmenttools, such as analytic assessment algorithms and functions, or“time-series scorers” for analyzing time-series data. The second programinstructions 112 are executable by the processing circuit 20 to causethe processing circuit 20 to engage the analytic assessment tools tomeasure characteristics-of-importance in a relevant analytic domain forsets of the time-series data. The relevant analytic domain may refer,for example, to information technology (IT) operations analytics, datacompleteness analytics and frequency/power/spectrum analytics. The thirdprogram instructions 113 are executable by the processing circuit 20 tocause the processing circuit 20 to generate, as a measurement result, ascore for each set of the time-series data based on the associatedcharacteristics-of-importance and, in some cases, external data. Thisexternal data may be, for example, meta-data relating to the sets of thetime-series data such as the source of the time-series data and acustomer name. The fourth program instructions 114 are executable by theprocessing circuit 20 to cause the processing circuit 20 to rank thesets of the time-series data in accordance with the score for each setof the time-series data for subsequent time-series data selection.

As defined herein, the characteristics-of-importance may include, butare not limited to, data completeness. That is, the second programinstructions 112 may be executable by the processing circuit 20 to causethe processing circuit 20 to engage the analytic assessment tools tomeasure whether a predefined percentage of expected data is/was presentfor a given time-series over a given window of time.

In accordance with embodiments, the third program instructions 113 maybe configured to cause the processing circuit 20 to combine multiplescores for each set of the time series data to thereby generate anoverall score. Such combining may be executed by the processing circuit20 by way of a linear combination of each of the multiple scores withnumerical weighting or by way of a binary combination of each of themultiple scores (for a binary combination, one approach is that, for atime-series, if any of the characteristics-of-importance exceed aspecified level for that characteristic, then that time-series would begiven a score of ‘1’, meaning ‘could be included’ such that the binaryscoring is typical of the first phase where we are looking forcandidates to include—whether it is actually included, depends onsubsequent scoring based upon analyzing a fuller set of data and forother key characteristics-of-interest). In the latter case, a score of“X” as a user configurable threshold would be required for a given setof the time-series data to be included in eventual analytics.

In accordance with further embodiments, the fourth program instructions114 cause the processing circuit 20 to include higher ranked sets of thetime-series data in eventual analytics. In doing so, the fourth programinstructions 114 may cause the processing circuit to 20 include sets ofthe time-series data having scores that are above a predefined thresholdin the eventual analytics and reject sets of the time-series data havingscores that are below the predefined threshold.

The ranking of the sets of the time-series data may be provided as atwo-phase or two-step process. In such cases, the initial (optional)phase may be executed as a scoring phase in which a relatively smalltime sample of data is examined and characteristics-of-interest aremeasured so that the results of the examination and measurement can beanalyzed using binary or weighted-combinations to arrive at an initialscore. In this first phase, a candidate subset is identified based uponessential criteria and is determined to be present in sufficientstrength for inclusion in the subsequent analytics. The second(characterization) phase follows where there may be fewer numbers ofsets of time-series data to be examined but those sets of time-seriesdata that remain would be across a wider range of times such that asingle stage process would otherwise be computationally expensive. Thecombination of scores for the characteristics-of-interest of the secondphase leads to the scores used for selection when exceeding configuredthresholds or ranking.

While it is understood that the first, second, third and fourth programinstructions 111, 112, 113 and 114 may be deployed by manual loadingthereof directly into a client, server and/or proxy computer by way of aloadable storage medium, such as a CD, DVD, etc., being manuallyinserted into each of the multiple computing devices 11, 12, 13, etc.,the first, second, third and fourth program instructions 111, 112, 113and 114 may also be automatically or semi-automatically deployed intothe computing system 10 by way of a central server 15 or a group ofcentral servers 15 (see FIG. 1). In such cases, the first, second, thirdand fourth program instructions 111, 112, 113 and 114 may bedownloadable into client computers that will then execute the first,second, third and fourth program instructions 111, 112, 113 and 114.

In accordance with alternative embodiments, the first, second, third andfourth program instructions 111, 112, 113 and 114 may be sent directlyto a client system via e-mail with the first, second, third and fourthprogram instructions 111, 112, 113 and 114 then being detached to orloaded into a directory. Another alternative would be that the first,second, third and fourth program instructions 111, 112, 113 and 114 besent directly to a directory on a client computer hard drive. When thereare proxy servers, however, loading processes will select proxy servercodes, determine on which computers to place the proxy servers' codes,transmit the proxy server codes and then install the proxy server codeson proxy computers. The first, second, third and fourth programinstructions 111, 112, 113 and 114 will then be transmitted to the proxyserver and subsequently stored thereon.

In accordance with embodiments and, with reference to FIG. 3, adeployment process of the computer program product described above isprovided. The process begins at block 300 and at block 101 with adetermination of whether the first, second, third and fourth programinstructions 111, 112, 113 and 114 will reside on a server or serverswhen executed. If so, then the servers that will contain the executablesare identified at block 209. The first, second, third and fourth programinstructions 111, 112, 113 and 114 for the server or servers are thentransferred directly to the servers' storage via FTP or some otherprotocol or by copying though the use of a shared file system at block210 such that the first, second, third and fourth program instructions111, 112, 113 and 114 are installed on the servers at block 211.

Next, a determination is made on whether the first, second, third andfourth program instructions 111, 112, 113 and 114 are to be deployed byhaving users access the first, second, third and fourth programinstructions 111, 112, 113 and 114 on a server or servers at block 102.If so, the server addresses that will store the first, second, third andfourth program instructions 111, 112, 113 and 114 are identified atblock 103 and a determination is made if a proxy server is to be builtat block 200 to store the first, second, third and fourth programinstructions 111, 112, 113 and 114. A proxy server is a server that sitsbetween a client application, such as a Web browser, and a real serverand operates by intercepting all requests to the real server to see ifit can fulfill the requests itself. If not, the proxy server forwardsthe request to the real server. The two primary benefits of a proxyserver are to improve performance and to filter requests.

If a proxy server is required, then the proxy server is installed atblock 201 and the first, second, third and fourth program instructions111, 112, 113 and 114 are sent to the (one or more) servers via aprotocol, such as FTP, or by being copied directly from the source filesto the server files via file sharing at block 202. Another embodimentinvolves sending a transaction to the (one or more) servers thatcontained the process software, and have the server process thetransaction and then receive and copy the process software to theserver's file system. Once the process software is stored at theservers, the users may then access the first, second, third and fourthprogram instructions 111, 112, 113 and 114 on the servers and copy tothe same to their respective client computer file systems at block 203.Alternatively, the servers may automatically copy the first, second,third and fourth program instructions 111, 112, 113 and 114 to eachclient and then run an installation program for the first, second, thirdand fourth program instructions 111, 112, 113 and 114 at each clientcomputer whereby the user executes the program that installs the first,second, third and fourth program instructions 111, 112, 113 and 114 onhis client computer at block 212 and then exits the process at block108.

At block 104, a determination is made as to whether the first, second,third and fourth program instructions 111, 112, 113 and 114 are to bedeployed by sending the first, second, third and fourth programinstructions 111, 112, 113 and 114 to users via e-mail. If a result ofthe determination is affirmative, the set of users where the first,second, third and fourth program instructions 111, 112, 113 and 114 willbe deployed are identified together with the addresses of the userclient computers at block 105 and the first, second, third and fourthprogram instructions 111, 112, 113 and 114 are sent via e-mail to eachof the users' client computers. The users then receive the e-mail atblock 205 and then detach the first, second, third and fourth programinstructions 111, 112, 113 and 114 from the e-mail to a directory ontheir client computers at block 206. The user executes the program thatinstalls the first, second, third and fourth program instructions 111,112, 113 and 114 on his client computer at block 212 and then exits theprocess at block 108.

Lastly, a determination is made on whether the first, second, third andfourth program instructions 111, 112, 113 and 114 will be sent directlyto user directories on their client computers at block 106. If so, theuser directories are identified at block 107 and the process software istransferred directly to the user's client computer directories at block207. This can be done in several ways such as, but not limited to,sharing the file system directories and then copying from the sender'sfile system to the recipient user's file system or, alternatively, usinga transfer protocol such as File Transfer Protocol (FTP). The usersaccess the directories on their client file systems in preparation forinstalling the first, second, third and fourth program instructions 111,112, 113 and 114 at block 208, execute the program that installs thefirst, second, third and fourth program instructions 111, 112, 113 and114 at block 212 and then exit the process at block 108.

With reference to FIG. 4, a method for selecting time-series data isprovided. The method includes assembling a set of analytic assessmenttools for time-series data, as shown at block 401, engaging the analyticassessment tools to measure characteristics-of-importance in a relevantanalytic domain for sets of the time-series data, as shown at block 402,generating, as a measurement result, a score for each set of thetime-series data based on the associated characteristics-of-importance,as shown at block 403 and as described above, and ranking the sets ofthe time-series data in accordance with the score for each set of thetime-series data for subsequent time-series data selection, as shown atblock 404 and as described above.

A potential use of the computer program product and the method describedabove would, in some cases, be to generate point tooling or “datamediation tooling” at potential data sources and data bases and, fortime-series data stored therein, to analyze the time-series data and topresent ranked lists to users for inclusion or exclusion with respect todownstream analysis. As noted above, aspects of the disclosure can beautomated whereby, for example, only time-series data scoring above acertain level might be flagged for subsequent inclusion in downstreamanalysis. Moreover, users will have an opportunity to adjust weightingvalues assigned to various factors and to build upon those supplied by apre-existing development team. For example, the user may decide thatparticular time-series data very directly measures key customerexperience aspects and are more important to include in downstreamanalytics then the corresponding score might otherwise indicate. Here,an example of a binary score would be if the time-series is directlyassociated with customer experience measurements (e.g., it is awell-known metric type), it may automatically be scored/flagged forinclusion based upon the best practice.

Advantages associated with the computer program product and methoddescribed above include, but are not limited to, the fact that thefeatures described herein can work without explicit notions of‘best-practices’ or pre-existing lists of metrics since importantfactors that are generally included in the creation of suchbest-practices or lists are codified within the time series scoringmechanisms. Thus, from day 1 of a product deployment, data selection canbe explicitly facilitated and can deal with vagaries of a particularenvironment (i.e., where a metric type X, based upon observation andanalysis, is determined to be acceptable in one environment but wouldnot be in another). In addition, administrators or users of the computerprogram product and method will be able to select the top-N sets of thetime-series data and have confidence that the set is a “best” fit for agiven case.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce acomputer-implemented process, such that the instructions which executeon the computer, other programmable apparatus, or other device implementthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of onemore other features, integers, steps, operations, element components,and/or groups thereof.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer program product for selectingtime-series data, the computer program product comprising a computerreadable storage medium having program instructions embodied therewith,the program instructions being readable and executable by a processingcircuit to cause the processing circuit to: assemble, by the processingcircuit, a set of analytic assessment tools for time-series data;engage, by the processing circuit, the analytic assessment tools tomeasure characteristics-of-importance in a relevant analytic domain forsets of the time-series data; generate, by the processing circuit, as ameasurement result, a score for each set of the time-series data basedon the associated characteristics-of-importance; and rank, by theprocessing circuit, the sets of the time-series data in accordance withthe score for each set of the time-series data for subsequenttime-series data selection.
 2. The computer program product according toclaim 1, wherein the program instructions cause the processing circuitto combine multiple scores for each set of the time series data togenerate an overall score.
 3. The computer program product according toclaim 2, wherein the combining by the processing circuit compriseslinearly combining each of the multiple scores.
 4. The computer programproduct according to claim 2, wherein the combining by the processingcircuit comprises binary combining of each of the multiple scores. 5.The computer program product according to claim 1, wherein the programinstructions cause the processing circuit to generate the score based onthe associated characteristics-of-importance and external data.
 6. Thecomputer program product according to claim 1, wherein the programinstructions cause the processing circuit to include higher ranked setsof the time-series data in an eventual analytics.
 7. The computerprogram product according to claim 6, wherein the program instructionscause the processing circuit to: include sets of the time-series datahaving scores that are above a predefined threshold in the eventualanalytics; and reject sets of the time-series data having scores thatare below the predefined threshold.
 8. A computer program product forselecting time-series data, the computer program product comprising: acomputer readable storage medium having stored thereon: first programinstructions executable by a processing circuit to cause the processingcircuit to assemble a set of analytic assessment tools for time-seriesdata; second program instructions executable by the processing circuitto cause the processing circuit to engage the analytic assessment toolsto measure characteristics-of-importance in a relevant analytic domainfor sets of the time-series data; third program instructions executableby the processing circuit to cause the processing circuit to generate,as a measurement result, a score for each set of the time-series databased on the associated characteristics-of-importance; and fourthprogram instructions executable by the processing circuit to cause theprocessing circuit to rank the sets of the time-series data inaccordance with the score for each set of the time-series data forsubsequent time-series data selection.
 9. The computer program productaccording to claim 8, wherein the third program instructions cause theprocessing circuit to combine multiple scores for each set of the timeseries data to generate an overall score.
 10. The computer programproduct according to claim 9, wherein the combining by the processingcircuit comprises linearly combining each of the multiple scores. 11.The computer program product according to claim 9, wherein the combiningby the processing circuit comprises binary combining of each of themultiple scores.
 12. The computer program product according to claim 8,wherein the third program instructions cause the processing circuit togenerate the score based on the associated characteristics-of-importanceand external data.
 13. The computer program product according to claim8, wherein the fourth program instructions cause the processing circuitto include higher ranked sets of the time-series data in an eventualanalytics.
 14. The computer program product according to claim 13,wherein the fourth program instructions cause the processing circuit to:include sets of the time-series data having scores that are above apredefined threshold in the eventual analytics; and reject sets of thetime-series data having scores that are below the predefined threshold.15. A computer-implemented method for selecting time-series data,comprising: assembling, by a processor, a set of analytic assessmenttools for time-series data; engaging the analytic assessment tools tomeasure characteristics-of-importance in a relevant analytic domain forsets of the time-series data; generating, as a measurement result, ascore for each set of the time-series data based on the associatedcharacteristics-of-importance; and ranking the sets of the time-seriesdata in accordance with the score for each set of the time-series datafor subsequent time-series data selection.
 16. The computer-implementedmethod according to claim 15, further comprising combining multiplescores for each set of the time series data to generate an overallscore.
 17. The computer-implemented method according to claim 16,wherein the combining comprises linearly combining each of the multiplescores.
 18. The computer-implemented method according to claim 16,wherein the combining comprises binary combining of each of the multiplescores.
 19. The computer-implemented method according to claim 15,wherein the generating comprises generating the score based on theassociated characteristics-of-importance and external data.
 20. Thecomputer-implemented method according to claim 15, further comprising:including higher ranked sets of the time-series data in an eventualanalytics; including sets of the time-series data having scores that areabove a predefined threshold in the eventual analytics; and rejectingsets of the time-series data having scores that are below the predefinedthreshold.